Conditional Generative Adversarial Network (CGAN) is a variation of GAN. It places a condition on generator and discriminator by feeding some extra information, \(y\) . This information could be data from other modalities. \(y\)  is fed into both generator and discriminator[1]}. The objective of the CGAN is expressed in eq:4.
\(\begin{split}\min _{G}\max _{D}V(D,G)= \mathbb {E}_{x\sim {P}_{r}}[\log (D(x|y))] + \mathbb {E}_{\tilde{x} \sim {P}_{z}}[\log (1-D(G(z|y))],\end{split}\) 
